RESUMEN
Cryo Electron Microscopy is a powerful Structural Biology technique, allowing the elucidation of the three-dimensional structure of biological macromolecules. In particular, the structural study of purified macromolecules -often referred as Single Particle Analysis(SPA)- is normally performed through an iterative process that needs a first estimation of the three-dimensional structure that is progressively refined using experimental data. It is well-known the local optimisation nature of this refinement, so that the initial choice of this first structure may substantially change the final result. Computational algorithms aiming to providing this first structure already exist. However, the question is far from settled and more robust algorithms are still needed so that the refinement process can be performed with sufficient guarantees. In this article we present a new algorithm that addresses the initial volume problem in SPA by setting it in a Weighted Least Squares framework and calculating the weights through a statistical approach based on the cumulative density function of different image similarity measures. We show that the new algorithm is significantly more robust than other state-of-the-art algorithms currently in use in the field. The algorithm is available as part of the software suite Xmipp (http://xmipp.cnb.csic.es) and Scipion (http://scipion.cnb.csic.es) under the name "Significant".